LEGS improves 3D Gaussian Splatting by replacing first-order edge guidance with second-order Laplacian structural guidance and nonlinear pixel-wise weighting, yielding up to 1.68 dB PSNR gain over baseline 3DGS on Tanks&Temples and Mip-NeRF360.
A water-tight and fully manifold 3D mesh of a [category] in [room type], no redundant parts, no missing parts, no holes, complete surface
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
FlowObject reformulates sparse-view 3D reconstruction as a training-free guided inverse problem in flow-matching models, augmented by 3DGS refinement to improve geometric completeness and fidelity.
citing papers explorer
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LEGS: Laplacian-Enhanced Gaussian Splatting with a Nonlinear Weighted Loss
LEGS improves 3D Gaussian Splatting by replacing first-order edge guidance with second-order Laplacian structural guidance and nonlinear pixel-wise weighting, yielding up to 1.68 dB PSNR gain over baseline 3DGS on Tanks&Temples and Mip-NeRF360.
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FlowObject: Flow Steering for Bridging Generative Priors and Reconstruction Fidelity
FlowObject reformulates sparse-view 3D reconstruction as a training-free guided inverse problem in flow-matching models, augmented by 3DGS refinement to improve geometric completeness and fidelity.